This module provides a foundational understanding of biostatistics, covering the various
statistical methods commonly employed in biomedical research. It introduces essential
concepts and terminology, emphasizing the critical role of statistical analysis in healthcare
and clinical research for drawing valid conclusions and making informed decisions.

This module focuses on techniques for summarizing and presenting data. Students will
learn to calculate and interpret measures of central tendency (e.g., mean, median, mode)
and dispersion (e.g., standard deviation, range). It also covers various graphical and chart
based methods for visualizing data, enabling effective communication of data
characteristics.

Building on descriptive statistics, this module delves into methods for making inferences
about populations based on sample data. Key topics include understanding probability
distributions, conducting hypothesis tests to assess claims, and constructing confidence
intervals to estimate population parameters. Students will gain practical experience with
common statistical tests such as t-tests, ANOVA, and chi-square tests.

This module introduces powerful statistical techniques for modeling relationships
between variables. It starts with linear regression, exploring how to predict one variable
based on another. It then progresses to multiple regression, allowing for the analysis of
relationships with multiple predictor variables, and covers essential aspects of model
building, assessment of model fit, and interpretation of regression coefficients.

This module specializes in analyzing time-to-event data, commonly encountered in clinical
trials and public health. It covers the unique characteristics of survival data, including
censoring, and introduces methods like Kaplan-Meier survival curves for visualizing
survival probabilities. The module also explores the Cox proportional hazards model, a
widely used technique for identifying factors influencing survival.

This module provides a comprehensive overview of the principles and methodologies
behind clinical trials. It covers crucial aspects such as randomization and blinding to
minimize bias and discusses methods for determining appropriate sample sizes and
various study design considerations essential for conducting robust and ethical research.

This module introduces the fundamental concepts and study designs used in
epidemiology, the study of disease patterns in populations. It covers measures of disease
frequency (e.g., incidence, prevalence) and measures of association (e.g., relative risk,
odds ratio). The module also focuses on interpreting epidemiological data and critically
assessing causal relationships.

This module provides practical, hands-on experience with statistical software packages
commonly used in biostatistics, such as R or SAS. Students will learn essential data
manipulation techniques and gain practical experience in applying various biostatistical
methods using these software tools for real-world data analysis.

This module explores the practical application of biostatistics within various healthcare
contexts. It examines how biostatistical principles are used in healthcare policy and
management, for evaluating the effectiveness of healthcare interventions and outcomes,
and for analyzing real-world healthcare data to inform evidence-based decision-making.

This culminating module provides students with the opportunity to apply the biostatistical
methods learned throughout the curriculum to a practical research project. It involves
designing and executing statistical analyses, critically interpreting the results, and
effectively presenting their findings in a comprehensive manner.